2019Journal of Irrigation and Drainage EngineeringRequires access

Grid-Based Model for Estimating Evapotranspiration Rates of Heterogeneous Land Surface

Mohanasundaram Shanmugam, Mesfin M. Mekonnen, Chittaranjan Ray

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Abstract

The complementary relationship areal evapotranspiration (CRAE) model estimates actual evapotranspiration (ET) based on potential and wet environment evapotranspiration rates. The existing CRAE-based models that calculate ET using point-based climatic inputs often adopt an interpolation method to produce spatially continuous ET grids at a regional scale. However, the effect of land surface heterogeneity on the ET estimation process is not well captured by these approaches. In the present study, the authors have developed a grid-based CRAE model (PY-CRAE) that accounts for the spatial heterogeneity in the land surface processes for estimating ET at a 4 km scale. The developed PY-CRAE model was applied for the entire state of Nebraska with monthly averaged climatic input data sets during the period 2000–2014. The performance of the PY-CRAE model along with the point-based CRAE model (WREVAP-CRAE) and a moderate resolution imaging spectro-radiometer (MODIS)-ET data products were assessed against water balance-based ET values. The results indicate that the PY-CRAE model performance was superior when compared to WREVAP-CRAE and MODIS-ET models with mean absolute error and root-mean-square error values of 40.94 and 49.15 mm/year, respectively.

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What this paper is about

The complementary relationship areal evapotranspiration (CRAE) model estimates actual evapotranspiration (ET) based on potential and wet environment evapotranspiration rates. The existing CRAE-based models that calculate ET using point-based climatic inputs often adopt an interpolation method to produce spatially continuous ET grids at a regional scale. However, the effect of land surface heterogeneity on the ET estimation process is not well captured by these approaches. In the present study, the authors have developed a grid-based CRAE model (PY-CRAE) that accounts for the spatial heterogeneity in the land surface processes for estimating ET at a 4 km scale. The developed PY-CRAE model was applied for the entire state of Nebraska with monthly averaged climatic input data sets during the period 2000–2014. The performance of the PY-CRAE model along with the point-based CRAE model (WREVAP-CRAE) and a moderate resolution imaging spectro-radiometer (MODIS)-ET data products were assessed against water balance-based ET values. The results indicate that the PY-CRAE model performance was superior when compared to WREVAP-CRAE and MODIS-ET models with mean absolute error and root-mean-square error values of 40.94 and 49.15 mm/year, respectively.

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Available abstract

The complementary relationship areal evapotranspiration (CRAE) model estimates actual evapotranspiration (ET) based on potential and wet environment evapotranspiration rates. The existing CRAE-based models that calculate ET using point-based climatic inputs often adopt an interpolation method to produce spatially continuous ET grids at a regional scale. However, the effect of land surface heterogeneity on the ET estimation process is not well captured by these approaches. In the present study, the authors have developed a grid-based CRAE model (PY-CRAE) that accounts for the spatial heterogeneity in the land surface processes for estimating ET at a 4 km scale. The developed PY-CRAE model was applied for the entire state of Nebraska with monthly averaged climatic input data sets during the period 2000–2014. The performance of the PY-CRAE model along with the point-based CRAE model (WREVAP-CRAE) and a moderate resolution imaging spectro-radiometer (MODIS)-ET data products were assessed against water balance-based ET values. The results indicate that the PY-CRAE model performance was superior when compared to WREVAP-CRAE and MODIS-ET models with mean absolute error and root-mean-square error values of 40.94 and 49.15 mm/year, respectively.

Key concepts: Evapotranspiration, Radiometer, Scale (ratio), Sunshine duration, Mean squared error, Environmental science, Remote sensing, Meteorology

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